{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#字段说明 \n",
    "#数据集共9个字段: \n",
    "#pregnants：怀孕次数 \n",
    "#Plasma_glucose_concentration：口服葡萄糖耐量试验中2小时后的血浆葡萄糖浓度 \n",
    "#blood_pressure：舒张压，单位:mm Hg \n",
    "#Triceps_skin_fold_thickness：三头肌皮褶厚度，单位：mm \n",
    "#serum_insulin：餐后血清胰岛素，单位:mm \n",
    "#BMI：体重指数（体重（公斤）/ 身高（米）^2） \n",
    "#Diabetes_pedigree_function：糖尿病家系作用 \n",
    "#Age：年龄 \n",
    "#Target：标签， 0表示不发病，1表示发病 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#                           数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1  调用工具包\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2  读取数据\n",
    "train = pd.read_csv(\"E://diabetes.csv \")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: (768, 9)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train:\",train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数值型特征的基本统计量\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "考虑到现实生活中某些列的值为0的话无意义，所以这里我们当作缺省项处理，具体有\n",
    "1、血浆葡萄糖浓度\n",
    "2、舒张压\n",
    "3、肱三头肌皮褶厚度\n",
    "4、餐后血清胰岛素\n",
    "5、体重指数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Glucose            5\n",
      "BloodPressure     35\n",
      "SkinThickness    227\n",
      "Insulin          374\n",
      "BMI               11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "Nan_col_names = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']\n",
    "print((train[Nan_col_names] == 0).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#3  查看每个变量的分布 及其与标签之间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#标签（Outcome）的分布\n",
    "#分类问题用countplot(用于整数的离散性或类别型)画出特征直方图\n",
    "sns.countplot(train['Outcome'])\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "特征的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#怀孕次数\n",
    "fig = plt.figure()\n",
    "sns.countplot(train['Pregnancies'])\n",
    "plt.xlabel('Number of pregnants')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "怀孕次数过多的情况下，可能是异常，判断是否为噪声点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbf260d2488>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Pregnancies',hue = 'Outcome',data = train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "由上图看出，怀孕次数和糖尿病患病率有一定联系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of occurrences')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#口服葡萄糖耐量试验中2小时后的血浆葡萄糖浓度 \n",
    "fig = plt.figure()\n",
    "sns.distplot(train.Glucose,kde = False)\n",
    "plt.xlabel('Glucose')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x = 'Outcome',y = 'Glucose',data = train,hue = 'Outcome')\n",
    "plt.xlabel('Diabetes',fontsize = 12)\n",
    "plt.ylabel('Glucose',fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "由上图可以看出，发病的范围主要集中在100到180这一大片范围内"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#舒张压\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.BloodPressure,kde = False)\n",
    "plt.xlabel('BloodPressure')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "由上图看到，舒张压为0也有部分数据，但这是不合常理的，考虑可能为缺失值或噪声点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x = 'Outcome',y = 'BloodPressure',data = train,hue = 'Outcome')\n",
    "plt.xlabel('Diabetes',fontsize = 12)\n",
    "plt.ylabel('BloodPressure',fontsize = 12)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#三头肌皮褶厚度\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.SkinThickness,kde = False)\n",
    "plt.xlabel('SkinThickness')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "由上图可知，三头肌皮褶厚度为0的缺失值较多，并且有一个较大的值，考虑为异常值，通常异常值表示发病的概率较高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x = 'Outcome', y = 'SkinThickness',data = train,hue = 'Outcome')\n",
    "plt.xlabel('Diabetes',fontsize = 12)\n",
    "plt.ylabel('SkinThickness',fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "由上图可知，发病的情况较为分散，未发病的情况分布较为集中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#餐后血清胰岛素\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.Insulin,kde = False)\n",
    "plt.xlabel('Insulin')\n",
    "plt.ylabel('frequency')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x = 'Outcome',y = 'Insulin',data = train,hue = 'Outcome')\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Insulin')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "由上图可知，发病率更为分散"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#BMI\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.BMI,kde = False)\n",
    "plt.xlabel('BMI')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x = 'Outcome',y = 'BMI',data = train,hue = 'Outcome')\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('BMI')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbf267ab1c8>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#groupby，分组。\n",
    "BMIDF = train.groupby(['BMI','Outcome'])['BMI'].count().unstack('Outcome').fillna(0)\n",
    "BMIDF[[0,1]].plot(kind = 'bar',stacked = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "由图中可知，BMI越大，糖尿病患病率越高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#糖尿病家系作用\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.DiabetesPedigreeFunction,kde = False)\n",
    "plt.xlabel('DiabetesPedigreeFunction')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbf26fb8bc8>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF = train.groupby(['DiabetesPedigreeFunction','Outcome'])['DiabetesPedigreeFunction'].count().unstack('Outcome').fillna(0)\n",
    "DF[[0,1]].plot(kind = 'bar',stacked = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'frequency')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#年龄\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.Age,kde = False)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "由上图可知，年轻的病例高于年老的病例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbf291c0548>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF = train.groupby(['Age','Outcome'])['Age'].count().unstack('Outcome').fillna(0)\n",
    "DF[[0,1]].plot(kind = 'bar',stacked = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "由上图可知，年轻人发病率在自身群体中较少，中年开始增长"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#4  特征与特征之间的相关性（热力图）\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbf2a11dc08>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(14,9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Gvgy8Dtw7gnzSUDj9ov1OVf0zsBqYmrPvXWALcBODVSulieRIXfudJKcABzBYPOngOQ/9OfAPVfXjwQKW0uSx1LW/2DWnDoMvp1hfVR/MLe+qehavetGEc5VGSWqIc+qS1BBLXZIaYqlLUkMsdUlqiKUuSQ2x1CWpIZa6JDXkfwGG/fjGlGMB6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1MPAg8KRdUt2uMUwfALy0/1P1yiRHzrO/ZcP2Uet+IcltST6V5BnTLmaS+iHaZwMbdtq117wWFukDWOZrYdpBv+RUCUO22ZMN8+/7C2B1Vf088L949C+cvUXrr4Fh3AI8paqeBfwx8D+nXM/EJHkc8DHg16rqOzvvnuchzb0WluiDZb8Wph30w0yV8KM2SfYFnkBbf+Iu2QdV9Y2q+kG/+mfAc3ZRbbuLvX5Kjar6TlU91C9/Etgvyaopl7XikuxHF3CXVNXH52nS/GthqT4Y5bUw7aAfZqqEq4Gz++XTgb+p/hOJRizZBzuNQZ5CN263N7kaOKu/4uIE4MGq2jbtonalJP98x2dTSY6j+3/3G9OtamX1/74Lgbur6g8XaNb0a2GYPhjltTDV74ytBaZKSPJ7wMaqupruH/2RJJvpzuTPnF7FK2/IPnh9klOAh+n64JypFTwBSS6ju5JgVZKtwPnAfgBV9SHgk3RXW2wGvge8ejqVTs4QfXA68CtJHgb+H3BmYyc8AM8DXgXckeTWfttbgaNgr3ktDNMHy34tOAWCJDVu2kM3kqQJM+glqXEGvSQ1zqCXpMYZ9JLUOINei0rySD9D3qb+lus3JnlMv282yfuXePw5Sf5kmcd86xj1Xpzkvr7mW5L8wjIf/1D/+6eTXDlqHcs43tuSfHVgJsJ3rvDzn5bk6QPrv5fkBSt5DO3+vLxSi0ryUFU9rl8+FLgU+FxVnT/k488BZqvqdaMcc4R6L6ab2e/KJP8aeE8/dcSuOPY+VfXIMh/zNuChqnrPKMcc4vkvpu+PSTy/9gye0WtoVbUdWAu8rr8z8cQkn4DuDr0kn0/yxf73MQMPPTLJX6Wbc/9HbxBJ/kOSv+vPZP80yT79Ge2B/bZLFmm3T3/2fmeSO5L8+jwl3wD8TP8cT+truDnJ3yb52X770Um+kOSmJL8/UNvq9HPDJzkoyRXpJpW7PN33Isz2+x7qz5I30E009Zwkn+mP89fp72pe6PgLSfKV9Le19385Xd8vvy3d3PXXJ7k3yesHHnNWX+NtST6S5Ll0d1K/u++7p/V9dnrf/qT+v9cd/XMeMHDst/d/Ed2xVK3aA0x7/mV/du8furPNnbd9C3gyA/NiA48H9u2XXwB8rF8+B9hGN+PogcCdwCzwc3STte3Xt/sAcNbOx1yoHd18P9cMtDu4/30xcHq/fAb9fOXAtcCafvl4uqk0oL+lvl8+b8exgdX0c8MDvwH8ab/8TLo7lGf79QJe1i/vB3wemOnX/z3dnc6LHf9twFeBW/ufF/XbvwKs6pdngesH2n8eOABYRXfr+37AM4B7Bh5zyM79MbgOPJZuFsh/0W//MN0EWjuO/av98muBC6b9OvRnvJ+pToGgPdZ8Mwg+AVifZA1d+O03sO+aqvoGQJKPA79IF5bPAW5KN23HgcD2eZ73pAXa/QXw1CR/DPwl8OmBx7w7ye8Ac8C56WYCfC7w53n0qwwO6H8/D3hpv/wR4F3z1PCLwPsAqurOJLcP7HuEbgIqgGPo3giu6Y+zD7BtieMDvLeWN3Tzl9VNcveDJNvp3nSfD1xZVV/v61xq4r9jgPuq6kv9+nq6N7o/6td3TKZ1M/DvllGbdkMGvZYlyVPpwm073dn2Dr8PXFdV/zbdPNrXD+zb+YOgonuzWF9Vb1nqkAu1S/Is4EV0AfUy4DX9rt+sgTHpJI8Hvl1V/2qBYyz1QdViX3Tz/Xp0XD7Apqr6sQ+Ahzj+fB7m0aHVx+607wcDy4/Q/X8cljdd71Jf3rPjGDueX3swx+g1tCQzwIfovtZw51B5At0QBPzkpGsvTHJIkgOB04DP0Q1lnN5/wEu//yl9+39MN1UrC7Xrx68fU1UfA/4L3dfwzau6+bzvS3JG/xzp3yToa9kxUd4rF3iKz9K9kZDuCpZ/uUC7e4CZ9Ff6JNkvyTOWOP5CvsKj01G/dJF2O1wLvCzJk/pjHNJv/y7dV9Lt7O+B1Ul+pl9/FfCZIY6jPZBBr6Xs+GB0E92XnnwaePs87f4A+K9JPkc3ZDHos3TDIrfSjd1vrKq7gN8BPt0PhVxD932ZAOuA25Ncski7w4Hr083wdzGw1F8Gr6QbxrkN2MSjX9f4BuC8JDfRvVnN5wN0AX478CbgdrpvOvsx1X0V5OnAu/rj3Eo3ZLPY8RfyduB9Sf6W7qx6UVW1CXgH8Jn+GDumuP0o8Jv9h65PG2j/fbqZH/88yR3AP9G9iatBXl4pLSHJPnQfBn+/D8tr6T7E/OGUS5OG4tibtLSDgOv64aQAv2LIa0/iGb0kNc4xeklqnEEvSY0z6CWpcQa9JDXOoJekxv1/j7MW+CrId/YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fu+hCulOvXtG3PS/Jh/tzcd+d5J4kl/X3nZPkX/uTjd07dVi8NGoMdx3tLqU7D/p/AE8mORv4XWAt8Gt0RxVeAD8719HfA5dV1TnAjcB1y1G0NJsVs3eRmraJ7uRn0B0Wvgk4FvhUVf0U+G6S+/v7zwDOAu7rThfCMXSnc5VGjuGuo1Z/zpJXA2clKbqwLp4958xhDwEerqoLlqhEacGcltHR7DLgpqp6cVWtrao1dL9o9ATwe/3c+ynARX3/3cBEkp9N0yR5+XIULs3GcNfRbBOHb6XfRvfjCpN0pwD+CN2ZSJ+qqv+l+4PwviRfBR6kO0e3NHI8K6Q0gyQvqKof9VM3X6T71abvLndd0lw55y7N7O7+B1aeD/y1wa5x45a7JDXIOXdJapDhLkkNMtwlqUGGuyQ1yHCXpAb9P9spzHDvW+3/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in train.columns:\n",
    "    sns.distplot(train[feature],kde = False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#           1数据探索&特征工程\n",
    "数据说明：\n",
    "第一列为怀孕次数 \n",
    "第二列为口服葡萄糖耐量试验中2小时后的血浆葡萄糖浓度 \n",
    "第三列为舒张压，单位:mm Hg \n",
    "第四列为三头肌皮褶厚度，单位：mm \n",
    "第五列为餐后血清胰岛素，单位:mm \n",
    "第六列为体重指数（体重（公斤）/ 身高（米）^2） \n",
    "第七列为糖尿病家系作用 \n",
    "第八列为年龄   \n",
    "对缺失的项（0）进行处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"E://diabetes.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "对无意义的0即缺失值进行处理\n",
    "在pandas中的DataFrame中，通过replace()函数可以很方便将我们感兴趣的数据子集的值标记为NaN\n",
    "标记完缺失值之后，可以利用isnull()函数将数据集中所有的NaN值标记为True，然后就可以得到每一列中缺失值的数量了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                   0\n",
      "Glucose                       5\n",
      "BloodPressure                35\n",
      "SkinThickness               227\n",
      "Insulin                     374\n",
      "BMI                          11\n",
      "DiabetesPedigreeFunction      0\n",
      "Age                           0\n",
      "Outcome                       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#把0标注成空值\n",
    "NaN_col_names = ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']\n",
    "train[NaN_col_names] = train[NaN_col_names].replace(0,np.NaN)\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                 0\n",
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "Outcome                     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#对缺失值用中值进行填补\n",
    "medians = train.median()\n",
    "train = train.fillna(medians)\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据标准化\n",
    "#分开特征和标签\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "y_train = train['Outcome']\n",
    "X_train = train.drop(['Outcome'],axis = 1)\n",
    "#用于保存特征工程之后的结果\n",
    "feat_names = X_train.columns\n",
    "#初始化特征的标准化器\n",
    "ss_X = StandardScaler()\n",
    "#分别对训练和测试数据的特征进行标准化处理\n",
    "X_train = ss_X.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#特征处理结果存储为文件\n",
    "train.to_csv('FE_Diabetes.csv',index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
      "0            6    148.0           72.0           35.0    125.0  33.6   \n",
      "1            1     85.0           66.0           29.0    125.0  26.6   \n",
      "2            8    183.0           64.0           29.0    125.0  23.3   \n",
      "3            1     89.0           66.0           23.0     94.0  28.1   \n",
      "4            0    137.0           40.0           35.0    168.0  43.1   \n",
      "\n",
      "   DiabetesPedigreeFunction  Age  Outcome  \n",
      "0                     0.627   50        1  \n",
      "1                     0.351   31        0  \n",
      "2                     0.672   32        1  \n",
      "3                     0.167   21        0  \n",
      "4                     2.288   33        1  \n"
     ]
    }
   ],
   "source": [
    "print(train.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#              2训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#正确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.7708333333333334\n",
      "{'C': 10, 'penalty': 'l1'}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#读取数据\n",
    "train = pd.read_csv('FE_Diabetes.csv')\n",
    "y_train = train['Outcome']\n",
    "X_train = train.drop(['Outcome'],axis = 1)\n",
    "#保存特征名字为可视化做准备\n",
    "feat_names = X_train.columns\n",
    "#需要调优的参数\n",
    "#将L1正则和L2正则分开，选择合适的调优算法\n",
    "penaltys = ['l1','l2']\n",
    "#训练数据多，C可以大一点（更多相信数据）\n",
    "Cs = [0.01, 0.1, 1, 10, 100, 1000, 10000]\n",
    "#组合调优参数\n",
    "tuned_parameters = dict(penalty = penaltys,C = Cs)\n",
    "lr_penalty = LogisticRegression()\n",
    "#正确率\n",
    "grid = GridSearchCV(lr_penalty,tuned_parameters,cv = 5,scoring = 'accuracy',return_train_score=True)\n",
    "grid.fit(X_train,y_train)\n",
    "#检验最佳模型\n",
    "#打印模型参数\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)\n",
    "\n",
    "#绘制CV误差曲线分析模型\n",
    "#plot CV误差曲线\n",
    "test_means = grid.cv_results_['mean_test_score']\n",
    "test_stds = grid.cv_results_['std_test_score']\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    plt.errorbar(x_axis, -test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    #plt.errorbar(x_axis, -train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'logloss' )\n",
    "plt.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "plt.show()\n",
    "\n",
    "import pickle\n",
    "\n",
    "pickle.dump(grid.best_estimator_, open(\"FE_Diabetes\", 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##log似然损失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "  FutureWarning)\n",
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      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
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      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\yzl\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
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      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4763675801909823\n",
      "{'C': 100, 'penalty': 'l2'}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#读取数据\n",
    "train = pd.read_csv('FE_Diabetes.csv')\n",
    "y_train = train['Outcome']\n",
    "X_train = train.drop(['Outcome'],axis = 1)\n",
    "#保存特征名字为可视化做准备\n",
    "feat_names = X_train.columns\n",
    "#需要调优的参数\n",
    "#将L1正则和L2正则分开，选择合适的调优算法\n",
    "penaltys = ['l1','l2']\n",
    "#训练数据多，C可以大一点（更多相信数据）\n",
    "Cs = [0.01, 0.1, 1, 10, 100, 1000, 10000]\n",
    "#组合调优参数\n",
    "tuned_parameters = dict(penalty = penaltys,C = Cs)\n",
    "lr_penalty = LogisticRegression()\n",
    "#log似然损失\n",
    "grid = GridSearchCV(lr_penalty,tuned_parameters,cv = 5,scoring = 'neg_log_loss',return_train_score=True)\n",
    "grid.fit(X_train,y_train)\n",
    "#检验最佳模型\n",
    "#打印模型参数\n",
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)\n",
    "\n",
    "#绘制CV误差曲线分析模型\n",
    "#plot CV误差曲线\n",
    "test_means = grid.cv_results_['mean_test_score']\n",
    "test_stds = grid.cv_results_['std_test_score']\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    plt.errorbar(x_axis, -test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    #plt.errorbar(x_axis, -train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'logloss' )\n",
    "plt.savefig('LogisticGridSearchCV_C.png' )\n",
    "\n",
    "plt.show()\n",
    "\n",
    "import pickle\n",
    "\n",
    "pickle.dump(grid.best_estimator_, open(\"FE_Diabetes\", 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "使用正确率和l2正则对Logistic回归模型的正则超参数调优可以使损失函数最小"
   ]
  }
 ],
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